Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural network that predicts optimal separation and compensation voltages for field asymmetric differential ion mobility spectrometry
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Summary
Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtually identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Separation can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate without need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users’ MRM-LC-ESI-DMS-MS/MS deployment.
Availability and implementation
Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS , https://zenodo.org/records/22214408 , https://github.com/Neurolipidomics/DMSDataExtractionToolkit , and https://ze-nodo.org/records/22777403 . Benchmarking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1 .
Contact
ldomic@uottawa.ca
Supplementary Information
Supplementary material is available at Bioinformatics online.
